Padhmanand Sudhakar
Kristel Van Steen
Amirul Islam Mallick
Kaline Arnauts- 1 Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India
- 2 BIO3 - Systems Genetics, GIGA Molecular & Computational Biology, Universite de Liege, Liège, Belgium
- 3 Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, India
- 4 Department of Chronic Diseases and Metabolism (CHROMETA), Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
Editorial on the Research Topic
Multi-scale systems: ecological approaches to investigate the role of the microbiota in different niches
Last decade’s rapid development of new technologies (such as Next-Generation Sequencing technologies (Reuter et al., 2015; Lightbody et al., 2019), enabling high-throughput molecular profiling) have underscored the role of microbial communities and their coordinated interactions within complex ecosystems. Niches in which such microbial ecosystems exist range from the majority of anatomical sites of the human and animal body to the deepest depths of the oceans, previously thought to be devoid of life itself (Yu et al., 2019; Dinan et al., 2015). Through our Research Topic titled “Multi-Scale Systems: Ecological Approaches to Investigate the Role of the Microbiota in Different Niches” hosted by Frontiers in Molecular Biosciences, we have attempted to highlight the systemic nature of microbiota, their diversity and activity, in particular in both health and disease conditions.
Since microbes coexist and interact within different ecological niches present in various kingdoms of life (Yu et al., 2019; Dinan et al., 2015; Gupta et al., 2021), integration of available datasets representing such interactions (Lapatas et al., 2015; Gomez-Cabrero et al., 2014) seemed of paramount importance. These strategies not only help build more representative models of biological reality but also aid amongst others in discovering the molecular mechanisms (Sudhakar et al., 2022), biomarkers (Zeng et al., 2016), key molecules/hubs (Li X. et al., 2019; He et al., 2014) which could drive phenotypically essential aspects such as response to therapeutic regimens (Chiu et al., 2018; Iorio et al., 2015; Chen et al., 2016), exploring disease heterogeneity in clinical settings (Sudhakar et al., 2021), amenability to biological interventions to ameliorate environmental degradation (Li L. et al., 2019; Ayilara and Babalola, 2023), susceptibility to biotic/abiotic stress (Gupta et al., 2021; Braga et al., 2016Braga et al., 2016), and disease resistance (Vannier et al., 2019).
Over the past decade or two, various tools and approaches (Pic et al., 2021; Meng et al., 2016; Rohart et al., 2017; Ruffalo et al., 2015) emerged for integrating high-throughput molecular-omic datasets. As a part of our Research Topic, the study by Agamah et al., demonstrated how an integrative approach fusing different -omics signatures such as transcriptomics, metabolomics, proteomics, and lipidomics with disease phenotypes revealed a cross-panel network of molecules (a.k.a. the interactome) driving different phases of COVID-19 associated disease severity. In particular, the interactome representative of mild COVID-19 cases was characterized by hubs such as CCL4, IRF1, HGF, MMP12, and IL10. In contrast, severe COVID-19 cases were characterized by a completely different hub set, including STAT1, SOD2, and metabolites such as diacylglycerol, lysophosphatidylcholine, taurine, sphingomyelin, and triglycerides. In a similar study by Wang and Lv., submitted to our Research Topic, the authors discovered causal associations between gut microbial taxa and metabolites derived from plasma to the progression of asthma. Multi-layered high-throughput profiling-based generation of -omic datasets enabled these findings, while their integration can leverage disease state-specific hubs and mechanisms for the discovery of novel drugs and drug targets.
Yet another challenge in addressing the complexity of microbial systems is the variation between individual samples and interpreting the biological significance of that variation with regards to their effects on phenotypic manifestations. In this insightful article by Melograna et al., they explored how Individual Specific Networks (ISNs) can be constructed from faecal microbiome profiles of patients with Inflammatory Bowel Disease (IBD) undergoing various biological therapies. The reverse-engineered ISNs from a population of subjects were able to capture the microbiome-based features predictive of response, but also network structures representing microbial interactions, which were associated with response to the therapeutic regimens under consideration.
From a real-world perspective, long-term studies provide enhanced data richness by capturing latent effects, which are particularly prevalent in microbe-rich niches subject to complex exposomic and environmental factors. Hence, long-term studies enable the identification of microbial shifts, including the nature of these shifts in terms of diversity, the temporal validity of biomarkers, and environmental drivers that promote alterations in composition. The study by Li et al., in our topic collection, demonstrates the efficacy of long-term sampling strategies, especially for diseases such as COVID-19, which have a highly dynamic nature due to the interplay of various factors, including diet, immune system, medications, co-infections, and comorbidities. In particular, the authors demonstrate that mild COVID-19 infections, even after recovery, have lasting impacts on the gut microbiota, as evidenced by the enrichment of probiotic taxa, including Blautia massiliensis and Kluyveromyces spp. three months post-recovery.
Last but not least, mechanistic discoveries add depth to studies by integrating microbiome-derived datasets with individual or combined -omic datasets. The studies by Tan et al., and Nie et al., demonstrate the utility of using large datasets and integrating them with curated phenotypic data to uncover key macromolecules associated with the phenotype of interest, or that could potentially mechanistically drive the phenotype. For example, Nie et al., uncovered increased susceptibility to IBD by using mice harbouring somatic mutations in the gene encoding EpCAM, a protein found in the basolateral membrane of Intestinal Epithelial Cells (IECs). By simulating colitis development via administration of dextran sulfate sodium (DSS) in both wild-type mice and mice with EpCAM deficiencies, followed by host inflammatory markers analysis as well as profiling of gut microbial alterations, the authors were able to pinpoint a set of gene-based and microbial markers associated with the link between EpCAM mutation and colitis development.
In line with the potential of high-throughput profiling technologies to generate microbial datasets and integrative -omic techniques to fuse such datasets with other -omic data types, the articles in our Research Topic collection have highlighted several possibilities, albeit the discovery of biomarkers, understanding mechanisms of pathogenesis, host response to microbial infections or uncovering temporal patterns in response to environmental stimuli. We hope such discoveries ignite renewed interest in the scientific community, as well as law/policymakers and the public to investigate further the roles played by microbes in health as well as disease across different scales - from the planet to the people and everything in between.
Author contributions
PS: Writing – original draft, Writing – review and editing. KV: Writing – review and editing. AIM: Writing – review and editing. KA: Writing – review and editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
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References
Ayilara, M. S., and Babalola, O. O. (2023). Bioremediation of environmental wastes: the role of microorganisms. Front. Agron. 5. doi:10.3389/fagro.2023.1183691
Braga, R. M., Dourado, M. N., and Araújo, W. L. (2016). Microbial interactions: ecology in a molecular perspective. Braz J. Microbiol. 47(Suppl. 1):86–98. doi:10.1016/j.bjm.2016.10.005
Chen, W. C., Yuan, J. S., Xing, Y., Mitchell, A., Mbong, N., Popescu, A. C., et al. (2016). An integrated analysis of heterogeneous drug responses in acute myeloid leukemia that enables the discovery of predictive biomarkers. Cancer Res. 76 (5), 1214–1224. doi:10.1158/0008-5472.CAN-15-2743
Chiu, Y.-C., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L.-J., et al. (2018). Predicting drug response of tumors from integrated genomic profiles by deep neural networks. arXiv. doi:10.1186/s12920-018-0460-9
Dinan, T. G., Stilling, R. M., Stanton, C., and Cryan, J. F. (2015). Collective unconscious: how gut microbes shape human behavior. J. Psychiatr. Res. 63, 1–9. doi:10.1016/j.jpsychires.2015.02.021
Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., et al. (2014). Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8 (Suppl. 2), I1. doi:10.1186/1752-0509-8-S2-I1
Gupta, S., Ray, S., Khan, A., China, A., Das, D., and Mallick, A. I. (2021). The cost of bacterial predation via type VI secretion system leads to predator extinction under environmental stress. iScience 24 (12), 103507. doi:10.1016/j.isci.2021.103507
He, F. Q., Sauermann, U., Beer, C., Winkelmann, S., Yu, Z., Sopper, S., et al. (2014). Identification of molecular sub-networks associated with cell survival in a chronically SIVmac-infected human CD4+ T cell line. Virol. J. 11, 152. doi:10.1186/1743-422X-11-152
Iorio, F., Shrestha, R. L., Levin, N., Boilot, V., Garnett, M. J., Saez-Rodriguez, J., et al. (2015). A semi-supervised approach for refining transcriptional signatures of drug response and repositioning predictions. PLoS ONE 10 (10), e0139446. doi:10.1371/journal.pone.0139446
Lapatas, V., Stefanidakis, M., Jimenez, R. C., Via, A., and Schneider, M. V. (2015). Data integration in biological research: an overview. J. Biol. Res. Thessal. 22 (1), 9. doi:10.1186/s40709-015-0032-5
Li, L., Lin, Q., Li, X., Li, T., He, X., Li, D., et al. (2019b). Dynamics and potential roles of abundant and rare subcommunities in the bioremediation of cadmium-contaminated paddy soil by Pseudomonas chenduensis. Appl. Microbiol. Biotechnol. 103, 8203–8214. doi:10.1007/s00253-019-10059-y
Li, X., Liu, Z., Mi, M., Zhang, C., Xiao, Y., Liu, X., et al. (2019a). Identification of hub genes and key pathways associated with angioimmunoblastic T-cell lymphoma using weighted gene co-expression network analysis. Cancer Manag. Res. 11, 5209–5220. doi:10.2147/CMAR.S185030
Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., et al. (2019). Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application. Brief. Bioinforma. 20 (5), 1795–1811. doi:10.1093/bib/bby051
Meng, C., Zeleznik, O. A., Thallinger, G. G., Kuster, B., Gholami, A. M., and Culhane, A. C. (2016). Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinforma. 17 (4), 628–641. doi:10.1093/bib/bbv108
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., and Droit, A. (2021). Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19, 3735–3746. doi:10.1016/j.csbj.2021.06.030
Reuter, J. A., Spacek, D. V., and Snyder, M. P. (2015). High-throughput sequencing technologies. Mol. Cell 58 (4), 586–597. doi:10.1016/j.molcel.2015.05.004
Rohart, F., Gautier, B., Singh, A., and Lê Cao, K.-A. (2017). mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput. Biol. 13 (11), e1005752. doi:10.1371/journal.pcbi.1005752
Ruffalo, M., Koyutürk, M., and Sharan, R. (2015). Network-based integration of disparate omic data to identify “Silent Players” in cancer. PLoS Comput. Biol. 11 (12), e1004595. doi:10.1371/journal.pcbi.1004595
Sudhakar, P., Andrighetti, T., Verstockt, S., Caenepeel, C., Ferrante, M., Sabino, J., et al. (2022). Integrated analysis of microbe-host interactions in Crohn’s disease reveals potential mechanisms of microbial proteins on host gene expression. iScience 25 (5), 103963. doi:10.1016/j.isci.2022.103963
Sudhakar, P., Verstockt, B., Cremer, J., Verstockt, S., Sabino, J., Ferrante, M., et al. (2021). Understanding the molecular drivers of disease heterogeneity in Crohn’s disease using multi-omic data integration and network analysis. Inflamm. Bowel Dis. 27 (6), 870–886. doi:10.1093/ibd/izaa281
Vannier, N., Agler, M., and Hacquard, S. (2019). Microbiota-mediated disease resistance in plants. PLoS Pathog. 15 (6), e1007740. doi:10.1371/journal.ppat.1007740
Yu, K., Pieterse, C. M. J., Bakker, PAHM, and Berendsen, R. L. (2019). Beneficial microbes going underground of root immunity. Plant Cell Environ. 42 (10), 2860–2870. doi:10.1111/pce.13632
Keywords: microbiota, microbiome, omic data integration, systems biology, biomarker discovery, microbial functions
Citation: Sudhakar P, Van Steen K, Mallick AI and Arnauts K (2025) Editorial: Multi-scale systems: ecological approaches to investigate the role of the microbiota in different niches. Front. Mol. Biosci. 12:1665390. doi: 10.3389/fmolb.2025.1665390
Received: 14 July 2025; Accepted: 23 July 2025;
Published: 08 August 2025.
Edited and reviewed by:
Michal Ciborowski, Medical University of Bialystok, PolandCopyright © 2025 Sudhakar, Van Steen, Mallick and Arnauts. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Padhmanand Sudhakar, cGFkaG1hbmFuZC5yLmJ0QGtjdC5hYy5pbg==